However, this discrepancy is not reflected in the traditional recall evaluation, since each word has an equal weight, regardless of its frequency of use.

we performed a frequency weighted recall evaluation.


In contrast to conventional methods that use dictionaries only, our method uses WordNet as a main resource of the pivot language to select the suitable translation pairs.

we eliminate most of the weaknesses caused by the structural differences of dictionaries, while profiting from the semantic relations provided by WordNet.


When looking up the meanings or translations of a certain head word, the user, whether he's a human or a machine, expects all translations to be accurate.

we evaluated 200 randomly selected Japanese entries from the initial translation candidates, together with all of their Hungarian translations, scoring them as correct (all translations are correct), acceptable (the good translations are predominant, but there are up to 2 erroneous translations), wrong (the number or wrong translations exceeds 2) or missing (the translation is missing) ( Table 7).


This method would only work if the languages are highly related, i.e., languages that share the same root.

we need another method to improve the robustness of MLDC model.


These use GNU Aspell, an open source spell checker (Atkinson, 2009), as a suggester component plugged into our system instead of our own Web-based suggester.

with Aspell, the suggestions and error scores proposed by the system would all derive from Aspell's handcrafted custom dictionary and error model.


We wanted to minimize manual tweaking particularly if it were necessary for every language.

heuristics such as the number of terms in the term list, the criteria for filtering triples, and the edit distance for defining close words were crude, and could easily be improved upon.


The Greeting and Signoff zones are sub-zones of the Author zone, usually appearing as the first and last items respectively in the Author zone.


However, extremely high costs are borne to manually enlarge and maintain such taxonomies.

applications using these taxonomies tend to face the drawback of data sparseness.


They used several kinds of features for the hyponymy relation candidate, such as a POS tag for each word, the appearance of morphemes of each word, the distance between two words in the hierarchical structures of Wikipedia, and the last character of each word.

of their experiments, approximately 2.4 million hyponymy relations in Japanese were extracted, with a precision rate of 90.1%.


sim(n trg ,n hypo ) is a distributional similarity between n trg and n hypo .

of scoring, each hypernym has a score for the target word.


( (Snow et al., 2005) is an exception to this.)

this work may give a misleading sense of progress.


This leads to the discovery of additional basic-level terms or instances, which are then recycled in turn to discover new intermediate concepts, and so on.

we can compare the results produced by the first iteration of our algorithm (before intermediate concepts are learned) to those of (Kozareva et al., 2008) for the Animal and People categories, and then compare again after 10 bootstrapping iterations of intermediate concept learning.


Equation 4 takes a sum across alignments, but most of the probability mass of p(s|a t (f )) will be concentrated on alignments in which predicates cover words that gloss them.

we can apply an approximation, in which we draw N samples in which predicates are aligned to their glosses whenever possible.


Like many heuristic-based pronoun resolvers (e.g., Mitkov (1998)), they first apply a set of constraints to filter grammatically incompatible candidate antecedents and then rank the remaining ones using salience factors.

their cluster-ranking model employs only factors that capture the salience of a cluster, and can therefore be viewed as a simple model of attentional state (see Grosz and Sidner (1986)) realized by coreference clusters.


If one training instance were created from each pair of mentions, the negative instances would significantly outnumber the positives, yielding a skewed class distribution that will typically have an adverse effect on model training.

only a subset of mention pairs will be generated for training.


all the ACE mentions are already known from the annotated corpus).

the actual effect of this joint anaphoricity-coreference ILP formulation on fully-automatic coreference resolution is still unclear.


If, in some situation, someone named "John" purposely hits someone named "Bill", then "John" is the agent and "Bill" is the patient of the hitting event.

given the predicate "hit" in both of the following sentences, "John" has the same semantic role of agent and "Bill" has the same semantic role of patient: 1) John hit Bill.


The statistics in the CoNLL 2004 and 2005 shared tasks (Shen and Lapata, 2007) shows that the semantic roles of ARG0/agent and ARG1/patient account for 85% of all arguments and most likely act as the centers of the local focus structure in discourse due to the close relationship between subject/object and agent/patient.

it is appropriate to model the centers of an utterance from the semantic perspective via semantic roles.


subject and object), using some heuristic rules.

it would be interesting to represent the centers of the utterances and employ the centering theory from the semantic perspective.


Although both this paper and Ponzetto and Strube (2006) employs semantic role features, their ways of deriving such features are much different due to different driving forces/motivations behind.

their contributions on coreference resolution are different: while the semantic role features in Ponzette and Strube (2006) captures the predicate-argument structure information and contributes much to common noun resolution and their contribution on pronoun resolution can be ignored, the centering-motivated semantic role features in this paper contribute much in pronoun resolution.


Indeed, the expectation is that in such corpus, the average perplexity is very low, and consequently, the great majority of cases can be coreferenced by a simple algorithm.

this test corpus may be largely ineffective in ranking the algorithms.


However, a target n-gram language model alone is known to be inadequate.

translation systems should also look at how the source sentence prefers to reorder.


order whatever input that it receives, during translation into English.

the results in the table also vary the reordering model used in Moses, set to either a single-parameter distance-based model, or to the lexicalized bidirectional msd model.


In a weighted matrix, each element that corresponds to a word pair is assigned a probability to measure the confidence of aligning the two words.

a weighted matrix is capable of using a lin-  ear space to encode the probabilities of exponentially many alignments.


We can estimate the relative frequencies for every word pair and obtain a weighted matrix shown in Figure 2(c).

each word pair is associated with a probability to indicate how well they are aligned.


Given a phrase pair (f j 2 j 1 , e i 2 i 1 ), we denote the inside area as in(j 1 , j 2 , i 1 , i 2 ) and the outside area as out(j 1 , j 2 , i 1 , i 2 ).

the inside probability of a phrase pair is calculated as China 's 1.0 0.24 0.24 China 1.0 0.24 0.24 's economy 0.4 0 0 Table 1: Some candidate target phrases of the source phrase "zhongguo de" in Figure 4, where α is inside probability, β is outside probability, and count is fractional count.


(2008) to remove duplicate alignments and retain only the alignment with the highest probability.

there were 550 candidate alignments on average for each sentence pair in the training data.


Features corresponding to aligned biphrases that occur inside aligned biphrases whose features are active in φ are also active in φ.

the set Φ x has a feature representation for all possible aligned sentence pairs (x, a , y ) that have x as the source side, so all reference translations y word-aligned to x in any way a are representable by features in Φ x .


We choose the a and y given by concatenating the target sides of the biphrases active inφ(x) in the order induced by their positions in the source sentence.

there is no phrase-level reordering, and the fluency of the target language output is induced by the phrase overlaps only.


For example, Sinuhe requires roughly one tenth of the memory used by Moses.

in terms of resource usage, Sinuhe trans and Sinuhe seem clearly superior to Moses.


It means that almost all the tree fragments embedded in the hyper-tree have corresponding translation rules.

we are able to use almost only one hyper-edge (i.e.


Finally, many of the phrases in WordNet are rare, including scientific names of animals and plants, genuses or families, which are not commonly used.

the clusters extracted here correspond to very salient sets of phrases.


The typical pattern for these terms is an almost flat time series, usually with small drops at summertime and Christmas (when seasonal leisure-related queries dominate).

these phrases were collected in just one cluster containing them all.


Since that the size of the right-hand-side and the number of outgoing d-edges per node are practically bounded by low constants, applying k rules on a tree T yields a linear increase in the size of the forest.

the resulting size is O(|T |+k), as we can see from Figure 3.


Since we have adopted a linear model, we are no longer constrained by the original generative story.

we are free to incorporate other SMT-inspired features: P E (t|s), target character count, and operation count.


Such annotation is far the most expensive part of the system design.

methods reducing the amount of labeled data can speed up and lower the overall amount of work.


We took into account the characteristics of the presented kernels and the structure of semantic annotated data.

we designed the tree structures shown in figures 2(a), 2(b) and 3 for STK and PTK and sequential structures for SK defined in the following (where all the structures refer to the same example presented in Section 3, i.e.


These are used to build annotation pairs, s i , s j , which are positive instances if s i has a lower concept annotation error than s j , with respect to the manual annotation.

a trained binary classifier can decide if s i is more accurate than s j .


More specifically, in our approach, we use tree fragments to establish the order of correctness between two alternative annotations.

given two trees associated with two annotations, a re-ranker based on tree kernel can be built in the same way of the sequence-based kernel by substituting SK in Eq.


We note that on a big corpus like MEDIA, the baseline models (FST and CRF) can be accurately learned thus less errors can be "corrected".

our re-ranking approach does not improve CRF but it still improves the FSTs baseline of 1.6% points (11.7% of relative improvement).


For this reason, there have been increasing needs for task specific rankings in web search such as rankings for specific query segments like long queries, time-sensitive queries, navigational queries, etc; or rankings for specific domains/contents like answers, blogs, news, etc.

a specific ranking task usually correspond to a category of queries; when the search engine determines that a query is belonging to this category, it will call the ranking function dedicated to this ranking task.


An important observation is that the relevance patterns for the ranking within a specific category may have some unique characteristics, which are different from generic relevance ranking.

it is reasonable to consider only using dedicated labeled training data and dedicated click preference data for training.


Since the success of phrase-based methods (Och and Ney, 2004;, models based on formal syntax (Chiang, 2005) or linguistic syntax Marcu et al., 2006) have also achieved state-of-the-art performance.

of the increasing numbers of available machine translation systems, studies on system combination have been drawing more and more attention in SMT research.


In a lattice, each edge is associated with a sequence of words rather than a single word.

we select phrases instead of words in each candidate set and minimize the chance to produce unexpected translations such as "be capable to".


The difference is that the hypothesis pairs are aligned just in one direction due to the expression limit of the confusion network.

the normalized alignments only contain 1-to-1 mappings (Actual null words are also needed in the case of null alignment).


Also due to 1-to-1 mappings, null word aligned to "is" is inserted.

"is" is missed in the output of IHMMbased model.


Note that since the algorithm only operates on the words that appear in the training data, we need to do something more to account for the unseen words.

to represent this type of question, we create the history tree structure depicted in Fig.


The second is that word se-quences in the partial hypotheses are used in the context of the models used in the subsequent decoding.

correctly decoding the start (or end for reverse decoding) of the sentence will benefit the subsequent decoding process.


(2006) found that the development of an automatic decision detection component is critical to the re-use of meeting archives.

with the new availability of substantial meeting corpora such as the ISL (Burger et al., 2002), ICSI (Janin et al., 2004) and AMI (McCowan et al., 2005) Meeting Corpora, recent years have seen an increasing amount of research on decision-making dialogue.


Anchored-SVM, however, does not require fine-tuning of the error-parameter C since it insures separability.

we learn anchored-SVM models quickly (few hours) as opposed to several days per model for C-tuned soft-margin SVM.


Anchored-SVMs also provide an easy explanation of the role of features in terms of separability.

we use anchored-SVMs in our experiments as the learning method, but we expect that other learning methods are capable of learning with the same reduced feature sets.


When we focus on the error for "Head selection of noun phrase" in cooccurring group (a), the correction of the error induced the rest of the errors in (a), and further induced the error in (b) according to the inductive relation from (a) to (b).

a "correction effect" for the error results in six errors.


Japanese explicitly marks arguments including the subject and object by postpositions, and is a headfinal language.

a verb phrase consisting of an object hon (book) and a verb yomu (read), for example, is expressed as hon-wo yomu (book-ACC read) "read a book" with the accusative postposition wo marking the object.


On the other hand, the purpose of using all of these tools is cooking, based on common sense.

probabilities such as P (r|cooking stove) and P (r|pan) should have large values.


Accordingly, Σ f ∈F l ∩Fr P (f |l)P (r|f ), Score base , should be relatively large for deep-fry → cook, Actually, we defined Score base based on the above assumption However, through a series of preliminary experiments, we found that the same score could be applied without losing the precision to the other postpositions.

we generalized the framework so that it could deal with most postpositions, namely ga (NOM), wo (ACC), ni (DAT), de (INS), and wa (TOP).


Fujita and Sato (2008) dealt with productive predicate phrases, while our target is non-productive lexical units, i.e., verbs.

this is the first attempt to apply a conditional probability based similarity measure to verb entailment acquisition.


However, we believe that dealing with verbs comprehensively, including infrequent ones, is important, since, in the era of information explosion, the impact on applications is determined not only by frequent verbs but also infrequent ones that constitute the long tail of a verb-frequency graph.

this tendency does not matter for our purpose.


The plots show that the cross-lingual measure of relatedness is not significantly affected by the reduction or increase of the vector length.

the use of vectors of length 10,000 (as used in most of our experiments) appears as a reasonable tradeoff between accuracy and performance.


For a CS of words, if we set the j-th word as an anchor word, the probability that all other words align to that word is: The alignment score of the whole CS is a weighted sum of the logarithm of the above alignment probabilities, i.e., and the global alignment score is computed as: Entropy model: In general, it is preferable to align the same word from different hypotheses into a common CS.

we use entropy to model the purity of a CS.


Its size is exponential in the length of the sentence and the number of hypotheses involved in combination.

pruning techniques are necessary to reduce the search space.


However, it is likely that these handcrafted keywords have poor coverage for general cases.

it may not make full use of WordNet semantic knowledge.


The reason behind this intention is that if the recall for any method is around 0.5, this means that the method fails to detect or correct around 50 percent of the errors.

we can not completely rely on these type of methods, for that we need some type of human interventions or suggestions to detect or correct the rest of the undetected or uncorrected errors.


As a result, we can not completely rely on these type of methods, for that we need some type of human interventions or suggestions to detect or correct the rest of the undetected or uncorrected errors.

if we have a method that can detect or correct almost 80 percent of the errors, even generating some extra candidates that are incorrect is more helpful to the human.


We normalize MCLCS z and call it normalized We take the weighted sum of these individual values v 1 , v 2 , v 3 , and v 4 to determine string similarity score, where α 1 , α 2 , α 3 , α 4 are weights and α 1 + α 2 + α 3 + α 4 = 1.

the similarity of the two strings, S ∈ [0, 1] is: We heuristically set equal weights for our ex- To give an example, consider s i = albastru and The string similarity, We determine the normalized frequency value of each candidate word for a single position with respect to all other candidates for the same position.


A spelling variation was defined as any word with an edit distance of 1 from the original word; that is, any single-character insertion, deletion, or substitution, or the transposition of two characters, that results in another real word.

none of the induced malapropisms were derived from closed-class words, and none were formed by the insertion or deletion of an apostrophe or by splitting a word.


A second contribution is that our model learns subtree features that constitute discriminative patterns: for example, variable length n-grams and partial dependency structures.

our model can capture both local features such as n-grams and non-local dependencies.


We also conjecture that the discrepancy between EMAIL and FORUM is probably due to the more heterogeneous nature of the FORUM data where anyone can post and reply while EMAIL (Enron or The improvement of less frequent tags is prominent, for example 25% for action motivator (AC), 40% for polite mechanism (P), and 15% for rhetorical question (QR) error rate reductions were achieved in FORUM data ( Figure 5(b)).

the semi-supervised learning method is more effective with small amounts of labeled data (i.e., less frequent annotations).


(9) (7) Confirmation Type: A positive character specified in a predefined set PC ensures that the opinion score of a word only comes from the other character.

the opinion score of this word is determined by the modified morpheme.


This T − value corresponds negative precision 0.70 and negative recall 0.02.

both positive and negative precision is approximately 0.7, which means that approximately 30% of the tags we assign to the examples are incorrect.


http://code.google.com/p/berkeleyparser/ 2 POSs are not counted in evaluating the performance of word-based syntactic parser, but they are counted in evaluating the performance of character-based parser.

the F1-measure for the later is higher than that for the former.


Based on K2, it is reasonable to believe that neighboring tokens tend to share the same label.

knowing that two tokens close to each other both belong to C l would strengthen our belief that either word is likely to have label l. We thus refer to this type of virtual evidence as collocation-based VE, and refer to C l as collocation lists.


However, restricted by the strong contextfree assumptions, the original PCFG model which simply takes the grammars and probabilities off a treebank, does not perform well.

a variety of techniques have been developed to enrich and generalize the original grammar, ranging from lexicalization to symbol annotation.


On the contrary, the noun POS tag and the verb POS tag are expected to have much more subcategories to express their context dependencies.

it is not reasonable to split them in the same way.


Results show that the words in the same subcategory of POS tags are semantic consistent in some cases.

it is expected to optimize the splitting and merging process at the POS level with semantic knowledge.


The extraction process blindly releases smaller TTS templates from the big templates, among which only a small fraction are correct TTS templates.

we need an inference method to raise the weight of the correct templates and decrease the weight of the noisy templates.


However, in Maui (as in Kea) this feature is just one component of the overall model.

if a candidate never appears as a keyphrase in the training corpus, it can still be extracted if its other feature values are significant enough.


first occurrence and spread, or node degree and semantic relatedness.

we also consider bagged decision trees, which can model attribute interactions and do not require parameter tuning to yield good results.


However, it is reasonable to assume that the set of VerbNet classes is fixed, because Levin-type classes are more static than ontological classes, like in WordNet synsets.

we can apply supervised classification methods to our task.


Model parameters can be estimated when training instances, i.e., pairs c, v , and features, f i (c, v), for each instance are given.

what we have to do is to prepare the training instances c, v , and effective features f i (c, v) that contribute to the better estimation of probabilities.


The difference is indicated in the last two frames of Sound Emission, although they appear much less frequently in real texts.

it is difficult to accurately capture the distinctions between these classes, if we are only provided with the statistics of the syntactic frames that appear in real texts.


As was mentioned in Section 2, transliteration can be helpful for languages with regular spelling differences.

we built a system for transliteration from Portuguese into Spanish that was trained on a list of automatically extracted likely cognates.


Although it is easy to construct a pt-es bi-text from the Europarl corpus, we chose not to do so since, in general, synthe-sizing a bi-text for X 1 -X 2 would be impossible: e.g., it cannot be done for ml-in given our training datasets for in-en and ml-en since the English sides of these bi-texts have no sentences in common.

we extracted the list of likely cognates between Portuguese and Spanish from the training pt-en and es-en bi-texts using English as a pivot as follows: We started with IBM model 4 word alignments, from which we extracted four conditional lexical translation probabilities: Pr(p j |e i ) and Pr(e i |p j ) for Portuguese-English, and Pr(s k |e i ) and Pr(e i |s k ) for Spanish-English, where p j , e i and s k stand for a Portuguese, an English and a Spanish word respectively.


Finally, we performed competitive linking (Melamed, 2000), assuming that each Portuguese wordform had at most one Spanish best cognate match.

using the values of Prod(p j , s k ), we induced a fully-connected weighted bipartite graph.


The process was repeated until there were no matchable pairs left.

of the above procedure, we ended up with 28,725 Portuguese-Spanish cognate pairs, 9,201 (or 32%) of which had spelling differences.


Training on the transliterated data and testing on Malay and Portuguese yielded about 10-12% relative decrease for Malay (lines 1-2) but 50% for Portuguese (lines 7-8).

10 unlike Spanish and Portuguese, there were far less systematic spelling variations between Malay and Indonesian.


A more effective solution is the application of convolution kernels to constituent parse trees (Zhang et al., 2006) but this is not satisfactory from a general per-spective since dependency structures offer some unique advantages, which should be exploited by an appropriate kernel.

studying convolution tree kernels for dependency trees is worthwhile also considering that, to the best of our knowledge, these models have not been previously used for relation extraction 1 task.


As mentioned in the previous section, we can engineer kernels by combining tree and sequence kernels.

we focus on the problem to define structure embedding the desired syntactic relational information between two named entities (NEs).


However, since each parse tree corresponds to a sentence, there may be more than two NEs and many relations expressed in a sentence.

the use of the entire parse tree of the whole sentence holds two major drawbacks: first, it may be too computationally expensive for kernel calculation since the size of a complete parse tree may be very large (up to 300 nodes in the Penn Treebank (Marcus et al., 1993)); second, there is ambiguity on the target pairs of NEs, i.e.


However, the set of the most probable implicit predicates is predictable from the semantics of the nouns.

(1a) plausibly means Als ich angefangen habe, dieses Buch zu lesen/schreiben... 'When I have started to read/write this book...' and (2a) plausibly means eine Frage die kompliziert zu beantworten ist 'a question which is complicated to answer'.


According to Pustejovsky the meaning of a noun includes a qualia structure representing "the essential attributes of an object as defined by the lexical item".

the lexical meaning of the noun book includes read and write as qualia roles.


In the proposed experimental setting argumentpredicate relations are defined in terms of the FrameNet frames.

we aim at extracting from SALSA tuples of the form Argument, ROLE, FRAME, Predicate such that the Argument plausibly fills the ROLE in the FRAME evoked by the Predicate.


The statement "Like RAD9, RAD9B associates with HUS1, RAD1, and RAD17" conveys factual information.

classifying the entire sentence as speculative will result in information loss.


For instance, consider the following sentences: 1.

it appears that the T-cell-specific activation of the proenkephalin promoter is mediated by NF-kappa B.


For example, "no evidence" is a multi-word keyword, where "evidence" is the parent of "no".

we extract the dependency parse tree features for the word "evidence".


Here, "whether" and "could" are speculation keywords and their co-occurence might be a clue for their speculative context.

we decided to include the co-occurring keywords to the feature set of a keyword.


Our approach is to develop probabilistic models that would provide a good framework for such studies.

we propose here a new model, ccLDA, which extends over the Latent Dirichlet Allocation (LDA) (Blei et al., 2003) and crosscollection mixture (ccMix) (Zhai et al., 2004) models on blogs and forums.


we propose here a new model, ccLDA, which extends over the Latent Dirichlet Allocation (LDA) (Blei et al., 2003) and crosscollection mixture (ccMix) (Zhai et al., 2004) models.


(3) We apply our new model on blogs and forums to identify cross-cultural differences.

different models can be compared to reflect different hypotheses about the data.


Because these two training procedures optimize different functions, we have normalized each trajectory by the final objective value at convergence.

the absolute values of this plot do not reflect the performance of either objective, but rather the smoothness with which the final objective is approached.


(2005) do semi-supervised learning over the query logs to classify queries into topics, but also train with hundreds of thousands of manually annotated queries.

while we also use the query logs and the identities of web pages of associated with each query, we are interested in finding methods that can be applied when that much annotation is prohibitive.


100 instances), the imbalance of relation types or manifold structure (cluster structure) in it will severely weaken the strength of bootstrapping.

it is critical for a bootstrapping approach to select the most appropriate initial seed set.


In this task, there is no guarantee that the annotators will identify the same set of expressions.

to measure agreement we want to consider how much intersection there is between the sets of expressions identified by the annotators.


Furthermore, since associative anaphors and their antecedents usually have different head nouns, string matching technique cannot be applied.

a large and diverse amount of lexical knowledge is essential to understand associative anaphora.


For example, the name of a country is considered as an indispensable entity of "shusho" (prime minister), but does not frequently appear as "N m ."

2 it is difficult to estimate a probability distribution and we use a hard decision.


While verbal case frames describe both obligatory and optional cases, nominal case frames describe only obligatory cases.

we consider all case slots of nominal case frames as the target of associative anaphora resolution.


On the other hand, although we expected to obtain higher precision with small decay rate, the highest precision was achieved by the decay rate 0.5.

we obtained the highest F-measure of 0.427 with the decay rate 0.5.


To help combat sparse counts for large numeric values, we use buckets of exponentially increasing width for larger numeric values.

between n = 0 and 10, buckets have size 1, between 10 and 100 they have size 10, and so on.


For the quantifier "a", we require that the relation have no argument clusters with size more than 1 for that class.

if Q q1 = "a", we restrict R m,n to R 1,n , and vice versa if Q q2 = "a."


To counteract this problem, we take a hybrid approach to parameter estimation, informed by both labeled training data and the extracted distributions.

crucially, our approach, which we call ZIPF FLATTENING, has only two parameters that need to be trained using a supervised approach, and these parameters do not depend on the relation R. the approach minimizes the amount of training data we need to a practical level.


Although the extracted data prevents us from determining that is capital of should be classified as a function, since almost all of the probability mass in P Right is still on n ∈ {0, 1}.

the probability for the direct reading of a sentence like "Some city is the capital of every country" is still very low.


In practice, it is often unreasonable to expect labeled data for every new domain that we come across, such as blogs, emails, a different newspaper agency, or simply articles from a different topic or period in time.

although [S+T+] is easier to handle, [S+T-] is of higher practical importance.


We find that the addition of not-a-name instances changes the problem -the not-a-names form most of the instances classified with high confidence.

we find that both standard and balanced bootstrapping fail to improve performance: the selection of the most confident instances no longer provide sufficient new information to improve performance.


In general, the dependent is a modifier or complement; the head plays a more important role in determining the behaviors of the pair.

cri-  teria of how to establish dependency relations and how to distinguish the head and dependent in such relations is central problem for dependency grammar.


5 Note that when |x c | becomes close to |x|, this time complexity actually approaches O(|x|).

to minimize this computational cost, x c is to be chosen from V c as follows: There are two issues with speeding up the classifier by the computation shown in Eq.


Note that the remaining features whose weights we sum up (line 4 in Algorithm 2) are primitive and conjunctive features that relate to f ∈ x − x c , which appear less frequently than f ∈ x c in the training data.

when we apply our algorithm to classifiers with the sparse solution (e.g., SVM-HKEs or 1 -LLMs), |x c | can be much smaller than the theoretical expectation (Eq.


The proposed framework leverages on the strengths of previous system combination and re-ranking techniques in parsing by integrating them into a linear model.

it is able to fully utilize both the logarithm of the probability of each k-best parse tree from each individual parser and any additional useful features.


Experimental results on AP-WSJ and TREC 2004 Novelty Track data show that the Chinese novelty mining performance is quite different when choosing two dissimilar POS filtering rules.

the selection of words to represent Chinese text is of vital importance to the success of the Chinese novelty mining.


To serve users better, it could be more helpful to further highlight novel information at the sentence level.

later studies focused on detecting novel sentences, such as those reported in TREC 2002-2004Novelty Tracks (Harman, 2002Soboroff and Harman, 2003;Soboroff, 2004), which compared various novelty metrics (Allan et al., 2003), and integrated different natural language techniques (Ng et al., 2007;Li and Croft, 2008).


Precision (P ), recall (R) and F Score (F ) are mainly used in evaluating the performance for sentence-level novelty mining (Allan et al., 2003).

we use RP , RR, RF and redundancy precision-recall (R-P R) curve to evaluate our experimental results on the document level.


Query can be considered as topic estimation of a unknown document .

by first randomly assigning topics to words and then performing a number of loops through the Gibbs sampling update, we have: where ,¬ counts the observations of word and topic in unseen document.


The situation is even worse when we adopted the 2 method, which was inferior in several cases.

the linear combination should be highly recommended.


Unfortunately, as models become more complex, for example through the addition of new features or components, dynamic programming algorithms can quickly explode in terms of computational or implementational complexity.

1 efficiency of inference is a critical bottleneck for many problems in statistical NLP.


The targeted use of the broadcast models is for parsing broadcast news transcripts for language models in speech recognition systems.

in addition to applying the transformations used for newswire, we also replaced symbolic expressions with verbal forms (e.g., $5 was replaced with five dollars) and removed punctuation and case.


We also employ a learning rate multiplier on the gradient.

the weight update for the ith encountered training sequence during training is The learning rate function is modelled on the one used by Finkel et al.


We also trained a model which combines both sets of features, but we found that we get better performance by training the two models separately, then averaging the models by computing the respective averages of their features' weights.

we use the model-averaged version of the models that consider context in the test set experiments.


Even if such clustering has inaccuracies, it could still be a useful starting point for human editors to select representative excerpts.

given a set of opinionated documents about a topic, we aim at automatically extracting and summarizing the multiple contrastive viewpoints implicitly expressed in the opinionated text to facilitate digestion and comparison of different viewpoints.


It has been shown that using syntactic information can improve the accuracy of sentiment models (Joshi and Rosé, 2009).

instead of representing documents as a bag of words, we will experiment with using features returned by a dependency parser.


We found that the clustering accuracy is improved if we measure the accuracy of only the subset of documents such that P (v|doc) is greater than some threshold (we used 0.8).

the accuraries presented in this section are measured using this confi-dence threshold.


Since the same excerpt may appear in multiple pairs, there would be significant redundancy in our reference summary if we were to include every pair.

we will restrict a contrastive reference summary to exclude overlapping pairs, and we will have many reference sets for all possible combinations of pairs.


Instead we will perform a simple qualitative evaluation to see if the algorithm appears to achieve its goal.

we asked 8 people to guess if each viewpoint's summary was written by Israeli or Palestinian authors.


For example, being fed, paid or adopted is generally desirable, but being chased, eaten, or hospitalized is usually undesirable.

we decided to create a lexicon of patient polarity verbs that produce positive or negative states for their patients.


chronological ordering of states for each character as the narrative progresses.

every affect state needs to be attributed to a character.


Most fables end with a moral, which is often a quote that may not mention the plot.

aESOP generates more spurious affect states from the quotations when using the gold standard annotations.


These systems are not very effective if the SMSes contain grammatical errors (or the system would require large amounts of training data in the language model to be able to deal with all possible types of noise) in addition to misspellings etc.

the translation of a cleaned SMS, into a second language, will not be very accurate and it would not give good results if such a translated SMS is used to query an FAQ collection.


Hence the two dictionary terms "good" and "guided" have the same LCSRatio of 0.5 w.r.t "gud", but the EditDistance SM S of "good" is 1 which is less than that of "guided", which has EditDistance SM S of 2 w.r.t "gud".

the similarity measure between "gud" and "good" will be higher than that of "gud" and "guided".


3, for each token we create a list of possible correct dictionary words by dictionary look up.

for token "cst" we get dictionary words lik "cost, cast, case, close".


This set was added to the training data and this helped familiarize the language model and phrase tables used by the MT systems to questions.

in total the MT system was trained on a corpus of 200, 000 sentences.


State of the art implementations of these linguistic analysis tasks are still limited in their performance, with errors that compound and propagate into the final performance of the QA system (Moldovan et al., 2002).

the performance of open domain QA systems has yet to arrive at a level at which it would become a feasible alternative to the current paradigms for information access based on keyword searches.


The initial arguments of the algorithm are the two focus words u i = f i and u r = f r .

the pair (f i , f r ) is the first pair of nodes to be added to the matching M in step 1.


In fact, these edges do not participate in any triangle (while they do participate in a square).

we would miss the computer science sense of the query.


Search result clustering can also be used to diversify the top results returned by a search engine.

for each query q, one natural way of measuring a system's performance is to calculate the subtopic recall-at-K (Zhai et al., 2003) given by the number of different subtopics retrieved for q in the top K results returned: where subtopics(r i ) is the set of subtopics manually assigned to the search result r i and M is the number of subtopics for query q (note that in our experiments M is the number of subtopics occurring in the 100 results retrieved for q, so S-recall@100 = 1).


The second constraint is on placing line breaks: they must come at word boundaries.

the 5th and 12th (and obviously 17th) syllable must end words.


Once a hypothesis drops out of the beam, it cannot be recovered, since no backtracking is done.

we cannot afford to explore a part of the hypothesis space which has no possible solutions for our constraints, we must be able to prune a hypothesis as soon as it leads us to such a subspace, otherwise we will end up on an unrecoverable garden path.


Second, we can note that whenever a source range is interrupted by a covered phrase and split into two ranges, the minimal and maximal sentence length is simply the sum of the corresponding lengths over the two uncovered subranges, plus the current hypothesis length.

if we precompute the minimum and maximum lengths over all ranges, using the same dynamic programming algorithm in advance, it is only necessary to iterate over the uncovered ranges (at most O(n), and O(1) in practice, due to reordering constraints) at runtime and sum their minimum and maximum values.


A naive approach, to use multiple feature functions, one with each pattern, does not work, since the decoder is using a (log-)linear model, in which costs are additive.

a pattern that matches one pattern, but not another, will still have high cost, perhaps as high as a pattern that partially matches both.


A more radical solution is to create an entirely different decoding algorithm which places words not left-to-right, or in a hierarchical fashion, but first placing words that must rhyme, and building hypotheses around them, like human translators of poetry do.

the system at present is too slow, and we cannot make it available online as a demo, although we may be able to do so in the future.


A majority of SSL algorithms are computationally expensive; for example, solving a transductive SVM exactly is intractable.

we have a conflict between wanting to use SSL with large unlabeled data sets for best accuracy, but being unable to do so because of computational complexity.


As the CRF is a discriminative model, the lack of label information renders the CRF weights independent of p(x) and thus we cannot directly utilize the unlabeled data when training the CRF.

semi-supervised approaches to training discriminative models typically use the unlabeled data to construct a regularizer that is used to guide the learning process (Joachims, 1999;Lawrence and Jordan, 2005).


Another drawback associated with such an approach is that the method encodes strong dependency assumptions between the punctuation symbol to be inserted and its surrounding words.

it lacks the robustness to handle cases where noisy or out-of-vocabulary (OOV) words frequently appear, such as in texts automatically recognized by ASR systems.


To counter this, we use the window w to select various feature subsets close to the top τ i features in ξ i .

the threshold values τ i and window size w should be approximated by experiments.


We use a 2x2 contingency table of a feature f and a class c to introduce In the table, W denotes the number of documents in the corpus in which feature f and class c cooccur, X the number of documents in which f occurs without c, Y the number of documents in which c occurs without f, and Z the number of documents in which neither c nor f occurs.

n = W + X + Y + Z is the total number of documents in the corpus.


Our experiment results showed that PN is still not sufficient or big enough for accurate PU learning.

we need to do a bit more work to find the final RN.


To our knowledge, however, there is no publicly available corpus that is annotated with such information.

we will resort to labeling a paragraph with its function label heuristically.


In fact, to our knowledge, there is no publicly-available corpus that is annotated with sentence function labels.

we adopt a heuristic approach to sentence function labeling.


The data set was provided by (Amini et al., 2009) as already-processed document vectors, using BM25 weighting.

we only test OPCA, CL-LSI, and related methods: JPLSA and CPLSA require modeling the term counts directly.


Furthermore, language model integration becomes more expensive here since the decoder now has to maintain target-language boundary words at both ends of a subtranslation (Huang and Chiang, 2007), whereas a phrase-based decoder only needs to do this at one end since the translation is always growing left-to-right.

syntax-based models are often embarassingly slower than their phrase-based counterparts, preventing them from becoming widely useful.


To add a bigram model, we split each −LM item above into a series of +LM items; each +LM item has the form (v, a ) where a is the last word of the hypothesis.

a +LM version of (1) might be: where the score of the resulting +LM item now includes a combination cost due to the bigrams formed when applying the phrase-pair.


This can be accomplished by introducing adapted nonterminal rules: in a revised generative process, the author can draw from Pitman-Yor distribution whose base distribution is over word sequences of arbitrary length.

4 in a setting where, say, K = 2, and our two classes are PROGRESSIVE and CONSERVATIVE, the sequence health care for all might be generated as a single unit for the progressive perspective, but in the conservative perspective the same sequence might be generated as three separate draws: health care, for, all.


To generate candidates for joint inference, we need to have multiple SRL results on each side of bitext.

for both Chinese and English SRL systems, we use the 3-best parse trees of Berkeley parser (Petrov and Klein, 2007) and 1-best parse trees of Bikel parser (Bikel, 2004) and Stanford parser (Klein and Manning, 2003) as inputs.


The second and third best parse trees of Berkeley parser are used for their good quality.

each monolingual SRL system produces 5 different outputs.


For example, a Chinese A0 argument aligns with an English A0 argument most of the times, and never aligns with an English AM * argument in Table 1.

the semantic role labels of arg s i and arg t k are used as a feature.


The English part of this data contains proposition annotations only for verbal predicates.

we only consider verbal predicates in this paper.


The Z-score is 2.35, i.e., the difference in performance is statistically significant with a confidence greater than 97.5%.

adding the specialized features is justified.


Unlike with words, the presence of a particular CSF in a sentence is not trivially recognizable.

there are three versions of the TSD task: (1) we are given the sentence, a marked subset of its words comprising a CSF, and the ASF that has generated these words; (2) we are given the sentence and a marked subset of its words comprising a CSF, without knowing the generating ASF; (3) we are given only the sentence and we need to find the contained CSFs and their ASFs.


The MFS baseline achieves on development data 42.9% and 13.2% in the TypeKnown and TypeUnknown conditions respectively 12 .

the algorithm outperforms the baseline both when the constrained model is used and when an unconstrained multi-class classifier is used.


Our TSD algorithm uses the SNOW algorithm, which is a sparse network of classifiers (Section 6).

it most resembles the ensemble approach to WSD.


This information can include accurate speech-recognition output, names which are recognizable even in wrong-language material, and clean target-language passages interleaved with the markup.

here we address methods to make IE robust to such varied-quality inputs.


Australia and country are mentions of type named and nominal, respectively, of a single geopolitical entity.

the MD task is a more general and complex task than named-entity recognition, which aims at identifying and classifying only named mentions.


Our approach to IE has been to use languageindependent algorithms, in order to facilitate reuse across languages, but we train them with languagespecific data, for the sake of accuracy.

input is expected to be predominantly in a target language.


Genre-specific training is typically infeasible due to such application-specific data sets being unannotated, motivating this line of research.

the goal of this study is to investigate schemes to make a language-specific MD engine robust to the types of interspersed non-target material described above.


An example with English as the target language is Barack Obama in the Spanish text ...presidente de Estados Unidos, Barack Obama, dijo el da 24 que ....

to minimize needless loss of processable material, a robustness algorithm ideally does a sliding analysis, in which, character-by-character or word-by-word, material may be deemed to be suitable to process.


However, nontarget character sets, such as Arabic and Chinese when processing English, represent pure noise for an IE system.

deterministic characterset segmentation is applied, to mark non-targetcharacter-set passages for non-processing by the remainder of the system, or, in a multilingual system, to be diverted to a subsystem suited to process that character set.


For each word, we run 1-through 6-preceding-word windows through the classifier, and 1-through 6-word windows beginning with the word, for a total of 12 windows, yielding for each window a result like: For each of the 12 results, we extract three features: the identity of the top-scoring language, here, Swedish; the confidence score in the top-scoring language, here, 0.235; and the score difference between the target language (English for these experiments) and the top-scoring non-target language, here, 0.148 − 0.235 = −0.087.

we have a 36-feature vector for each word.


be processed in a garbage-in-garbage-out fashion merely because the system was designed only to handle clean text in one language.

we have embarked on information-extraction-robustness work, to improve performance on imperfect inputs while minimizing disruption of processing of clean text.


Furthermore, the imbalance of different classes and the inherent structural complexity of instances will severely weaken the strength of bootstrapping and semi-supervised learning as well.

it is critical for a bootstrapping procedure to select an appropriate seed set, which should be representative and diverse.


Perform intra-strata sampling to form SeedSET i from each stratum RSET i , by selecting the center C i and (N i -1) additional instances Generate SeedSET by summating RSET i from each stratum Nevertheless, remember the fact that, this time for the stratum RSET i , the center C i has been chosen, so it may not be reasonable to extract additional centers in this way.

in order to avoid recursion and over-complexity, we employ a diversity-motivated intra-stratum sampling scheme (Shen et al., 2004), called KDN (K-diverse neighbors), which aims to maximize the training utility of all seeds from a stratum.


And this could be further explained by our observation that, with the increase of cluster numbers, the clusters get smaller and denser while their centers also come closer to each other.

the representativeness and diversity as well as the distribution of the seeds sampled from them may vary accordingly, leading to different performance.


Although hierarchical clustering is quadratic, we can efficiently exploit the agglomerative process as the most similar terms will merge into clusters first.

to identify the k most similar terms, we can exit the clustering process as soon as a cluster of size k is established.


In LDA, θ (d) and φ (z) are drawn from conjugate Dirichlet priors α and β, separately.

θ and φ are integrated out and the probability of word w given document d and priors is represented as follows: where K is the number of topics.


Given the topics of the document d and a word w, We have used various methods to com-pute similarity including cosine similarity, predictive likelihood and KL-divergence (Heinrich, 2005), among which cosine similarity performs the best on both datasets.

we only show the results of the LDA baseline calculated using cosine similarity.


Using the decomposition in Equation 3, it is clear that the only component of the joint labeled document probability which relies upon the task parameters is log P φ (y * |s, T ).

for the M-Step, it is sufficient to optimize the following with respect to φ: The first equality follows from the decomposition of the task component into independent CRFs (see Equation 2).


In this data set, segments corresponding to each of the aspects are clearly delineated in each document.

we can compare the performance of the algorithm using automatically induced content models against the gold standard structural information.


While extensive research has been conducted in topic segmentation for monologues (e.g., (Malioutov and Barzilay, 2006), (Choi et al., 2001)) and synchronous dialogs (e.g., (Galley et al., 2003), (Hsueh et al., 2006)), none has studied the problem of segmenting asynchronous multi-party conversations (e.g., email).

there is no reliable annotation scheme, no standard corpus, and no agreedupon metrics available.


In our extension of LCSeg, we use a similar method to consolidate different segments; however, in our case the linearity constraint is absent.

we approximate the optimal solution by spectral clustering (Shi and Malik, 2000).


There are no publicly available email corpora annotated with topics.

the first step was to develop our own corpus.


Again, our problem of topic segmentation for emails is not sequential in nature.

the standard metrics widely used in sequential topic segmentation for monologues and dialogs, such as P k and W indowDif f (W D), are also not applicable.


It also increases with the increase of the number of topics.

it is a measure of how specific an annotator is and in our dataset it varies from 0 6 to 2.7.


In fact in our corpus we found an average quotation usage of 6.44 per thread.

we need to leverage this useful information in a principled way to get the best out of our models.


We want to regularize the LDA model such that two connected words u, v have similar topicword distributions (i.e., φ Note that the standard conjugate Dirichlet prior on φ is limited in that all words share a common variance parameter, and are mutually independent except normalization constraint (Minka, 1999).

it does not allow us to encode this knowledge.


Note that a fragment can be in multiple paths (e.g., f , g, in Figure 2) which will cause its sentences to be in multiple segments found by LCSeg.

as a final step we need a consolidation method.


A fundamental problem that one encounters when trying to learn a classifier for a large number of relations with small training dataset is that most of the features that appear in the test instances either never occur in training instances or appear a small number of times.

the classification algorithm does not have sufficient information to correctly predict the relation type of the given test instance.


However, in the RST framework, only one relation is allowed to hold between two EDUs.

each instance from the RSTDT is labeled with a single discourse relation, from which a single feature vector is created.


If S:LITERAL chooses (a), L:LITERAL will guess the target object O1 correctly with probability 1 2 ; if S:LITERAL chooses (b), L:LITERAL will guess correctly with probability 1.

the expected utility EU(S:LITERAL, L:LITERAL) = 3 4 .


For resource-limited language pairs, coverage of the test set by the parallel corpus is an important factor that affects translation quality in two respects: 1) out of vocabulary words; 2) the same information in an input sentence can be expressed in different ways, while current phrase-based SMT systems cannot automatically select an alternative way to transfer the same information.

given limited data, in order to facilitate translation from the input side, this paper proposes a novel method to reduce the translation difficulty using source-side lattice-based paraphrases.


(2006) argue that limited amounts of parallel training data can lead to the problem of low coverage in that many phrases encountered at run-time are not observed in the training data and so their translations will not be learned.

in recent years, research on addressing the problem of unknown words or phrases has become more and more evident for resource-limited language pairs.


Therefore, for all the "ORG-E" edges, their weights in the lattice are set to 1.0 as the reference.

in the log-linear model, decoding paths going though these edges are not penalised because they do not come from the paraphrases.


, g k }, then the weights are calculated as in (4): where k is a predefined parameter to trade off between decoding speed and the number of potential paraphrases being considered.

once a decoding path goes though one of these edges, it will be penalised according to its paraphrase probabilities.


The more edges there are in a lattice, the more complicated the decoding is in the search process.

in order to reduce the complexity of the lattice and increase decoding speed, we must filter out some potential noise in the paraphrase table.


However, one of the major challenges is that all the current models only allow one-to-one mapping from one source frontier non-terminal node (Galley et al., 2004) to one target frontier non-terminal node in a bilingual translation rule.

all those translation equivalents with one-to-many frontier non-terminal node mapping cannot be covered by the current state-of-the-art models.


On the other hand, due to the data sparseness issue and model coverage issue, previous tree-to-tree (Zhang et al., 2008;Liu et al., 2009) decoder has to rely solely on the span information or source side information to combine the target syntactic structures, without checking the compatibility of the merging nodes, in order not to fail many translation paths.

this solution fails to effectively utilize the target structure information.


But they cannot extract rule 1 and rule 2.

for any tree-to-string rule, our method can always find the corresponding tree-to-tree sequence rule.


Therefore, for any tree-to-string rule, our method can always find the corresponding tree-to-tree sequence rule.

our rule coverage is the same as tree-to-string framework while our rules contain more informative target syntax information.


We rely on the explicit hyper-edges to enumerate possible hypothesis while using the latent hyper-edges to measure its translation probability and syntax goodness.

the complexity of the search space is reduced into the tree-to-string model level, while keeping the target language generation syntactic aware.


The toplevel weights are trained to maximize a metric such as BLEU on a small development set of approximately 1000 sentence pairs.

provided at least this amount of IN data is available-as it is in our setting-adapting these weights is straightforward.


The boxed diagonal line segment arises from the repeat of the word "processing," while the main diagonal line trivially arises from self-similarity.

the search for line segments in K off the main diagonal provides a simple algorithmic means to identify repeated terms of possible interest, albeit sometimes partial, in a collection of text documents.


The only requirement is that the same target language phoneme spoken by distinct speakers map to similar posterior distributions over the reference language phoneme set.

even though we evaluate the system on matchedlanguage Switchboard data, it can be just as easily applied to any target language with no languagespecific knowledge or training resources required.


the users position in the environment) in order to resolve.

the focus of our work here is on exploring these complex user behaviors in situated dialogue and examining how to combine eye gaze with ASR hypotheses for improved reference resolution.


Moreover, in utterances with multiple referring expressions, there may not be a single recognition hypothesis that contains all referring expressions, but each referring expression may be contained in some recognition hypothesis.

it is desirable to consider the entire n-best list of hypotheses.


For applications in which it is critical to identify the objects referred to by the user, precisely identifying uttered referring expressions may be unnecessary.

we evaluate the reference resolution algorithm with a lenient comparison of (referring expression, referent object set) pairs.


Without eye gaze information, the semantic compatibility alone could be insufficient to resolve this referring expression.

when eye gaze information is incorporated, the main source of performance improvement comes from better identification of potential referent objects.


If a referring expression with a high compatibility value to some potential referent object is present in a lower probability hypothesis, this referring expression can only be identified when a WCN rather than a 1-best hypothesis is utilized.

when word confusion net-works are incorporated, the main source of performance improvement comes from better referring expression identification.


However, when these sentences are combined into summaries it is not guaranteed that these summaries will also have high content overlap with the entire model ones.

we believe if there is a high content agreement between the training and model summaries this could lead to more readable summaries.


Note that these alignments are noisy (e.g., [mean] aligns to a period).

for each record (skyCover and temperature in this case), we extract two templates: (1) a COARSE template, which takes the text spanned by the record and abstracts away all fields in the scenario ( For each record r i , we define T i so that BASE(T i ) and COARSE(T i ) are the corresponding two extracted templates.


(2009) to automatically produce aligned training scenarios (Section 4.3.1) was less effective than it was in the other two domains due to two factors: (i) there are fewer training examples in SUMTIME and unsupervised learning typically works better with a large amount of data; and (ii) the alignment model does not exploit the temporal structure in the SUMTIME world state.

we used a small set of simple regular expressions to produce aligned training scenarios.


2007;Das and Smith 2009), our approach operates over a combined PCFG-dependency representation.

some configurations in Smith and Eisner (2006) are not so relevant here -instead, we found that deletions, reorderings, flattening of nodes, and the addition of text elements were im-  Figure 1 shows some example alignments that are captured by the QG, with the source node on the left and the target node on the right.


For example, we would be happy for an iambic generator to output big thoughts are not quite here, but not quite big thoughts are not here.

we take a different tack and apply unsupervised learning to acquire word-stress patterns directly from raw poetry, without relying on a dictionary.


With this new rule type, G 2 allows phrase/rule concatenation before reordering with another hierarchical rule.

nested reordering does not require successive swaps anymore.


Phrasal rule captures short distance reorderings within phrases, while hierarchical rule captures long distance reorderings be- tween phrases.

the HPB model outperforms conventional phrase-based models on phrase reorderings.


This is different from , in which they built a classifier for each ambiguous hierarchical sourceside.

the training examples for each MaxEnt model is small and the model maybe unstable.


In addition, we extend the HPB model with BTG, that adding an inverted glue rule to merge phrases inversely (Section 2.2).

the glue rules are classified into two patterns: serial or inverse.


As described above, we classified phrase reorderings into several patterns.

phrase reordering can be regarded as a classification problem: for each source reordering pattern, we treat the corresponding target reordering patterns as labels.


However, our extensive experiments with this approach (omitted here) show that it suffers from a number of flaws, such as a high false positive rate if all features are tracked, the difficult problem of identifying an informative subset of features for tracking, and deciding how many such features need to change before a shift has occurred, which turns out to be highly variable between shifts.

our goal is to use a single A-distance tracker by collapsing each example to a single value.


However, at any one point the accuracy displays considerable variance.

while classification accuracy clearly suffers, it is difficult to measure this even in a supervised setting with labeled examples when considering a small portion of the stream.


Both sides can contain non-terminals, and source and target non-terminals are one-to-one aligned.

in the decoding phase, non-terminal replacement for both sides are synchronized.


For example, we may use the second rule in Figure 1.

hATE will be translated into hates, which is wrong.


This is why we named the two families Transitive 3 and Intransitive 2 in Figure 4.

we approximate bi-lingual families with English families first.


We use the families of the English word as the families of bi-lingual lexical items.

we have a list of about 20 families and an association table as described in Section 3.2.


Second, the introduction of complementary triggers implies that evaluation of p(u, v) and p(v, u) now involves disjoint sets of feature functions.

p(u, v) is not guaranteed to satisfy the complementarity condition imposed on h(u, v), and therefore cannot directly be used as the binary pairwise comparator.


For each pair of sentences (u, v) : u < v in D , we generate two training entries.

the first, signifying that u appears before v in D , assigns the label true to a trigger list consisting of standard feature functions derived from u, and complementary feature functions derived from v. the second, reinforcing this observation, assigns the label false to a trigger list consisting of complementary feature functions from u, and standard feature functions from v. the labeled training set (feature:label pairs) for the comparator can be expressed as follows: if there are d sentences in D , we obtain a total of d(d − 1) labeled examples to train the comparator.


Using the number of times f is paired with some target phrase e i , count(f, e i ), relative frequency estimation can be used to compute the probability of translation e i given source phrase f : This value, together with other estimates of how appropriate a translation pair (f, e i ) is, are recorded in a phrase table, which typically discards all contextual information.

8 the translation distribution of some phrase is globally estimated from a training corpus independently of the actual context of that phrase.


• Contrarily to the examples of p 2 translating as e 6 , examples of p 3 translating as e 4 are much more likely of being contextually-appropriate with f , meaning that f could be substituted with most p 3 examples.

e 4 , which was not initially considered as a possible translation of f , could now be considered as such.


While there are improvements on both SysCombTune and Test, the results on SysCombTune are not totally fair since it was used for tuning system combination weights and as validation for optimizing weights of the MT systems.

our discussion will focus on results on Test.


All of the commonly used statistical parsers use context-free dynamic programming algorithms and as such work bottom up on the entire sentence.

they only find a complete fully connected parse at the very end.


In contrast human syntactic parsing must be fully connected (or close to it) as people are able to apply vast amounts of real-world knowledge to the process as it proceeds from word-toword (van Gompel and Pickering, 2007).

any parser claiming cognitive plausibility must, to a first approximation, work in this left-toright top-down fashion.


However in Penn-treebank grammar style an initial NP could also be a possessive NP as in (S (NP (NP (DT The) (NN dog) (POS 's)))) Clearly this NP is not the subject.

using left corner transforms on all NP's allows the parser to conflate differing semantic situations into a single tree.


The intuition behind this algorithm is that different sets of sentences are likely to manifest different syntactic patterns.

the best way to learn the syntactic patterns of any given set of sentences might be to train the parser on the sentences contained in the set.


The parser we experiment with is the incremental parser of Seginer (2007), whose input consists of raw sentences and does not include any kind of supervised POS tags (created either manually or by a supervised algorithm).


The fully trained parser is therefore also a valuable member in the ensemble.

we introduce an extended zoomed learning algorithm (EZL).


The algorithm then goes over each individual tree in the set I and scores it according to the collected statistics The PUPA algorithm is guided by the idea that syntactic structures that are frequently created by the parser are more likely to be correct than structures the parser produces less frequently.

constituents that are more frequent in the set I receive higher scores after proper regularization is applied to prevent potential biases.


Confidence-based ZL is based on the idea that sentences for which the fully-trained parser provides parses of similar quality manifest similar syntactic patterns.

the parser performance on a set of such sentences can be improved if it is trained only with the sentences contained in the set.


we estimate β usinĝ For the second stage, we assume that the coefficients for each feature can be shrunk differently, but that coefficients for each feature should be shrunk the same regardless of what label they are predicting.

for a given observation we predict: where the weights w j indicate how much to shrink each of the p features.


since each label l is a set of 15 categorical variables, each y in the first learning stage is actually a vector with 16 nonzero components and thousands of zeros.


To understand why, it is necessary to remember that the semantic model uses only semantic features -probabilities of rule applications are not considered.

the semantic model is perfectly happy to predict derivations with sequences of highly unlikely rule applications so long as they predict a role that the model has been trained to prefer.


The binary tree produced by our method groups the contexts of a polysemous word at different heights of the tree.

it induces the senses of that word at different levels of sense granularity.


All the described methods for sense induction ap-  ply flat graph clustering methods to derive the clusters (senses) of a target word.

they neglect the fact that their constructed graphs often exhibit a hierarchical structure that is useful in several tasks including word sense disambiguation.


In this setting, the base corpus (bc) (Section 3.1) for a target word consists both of the training and testing corpus.

a testing context c j of tw is a leaf in the generated dendrogram.


Given two vertices (contexts) c i and c j , let N (c i , c j ) be the set of their neighbours and K(c i , c j ) be the set of edges between the vertices in N (c i , c j ).

the maximum number of edges that could exist between vertices in the similarity of c i , c j is set equal to the number of edges that actually exist in that neighbourhood divided by the total number of edges that could exist ( ).


This means that a number of words in the bag-of-words context vectors and collocations in the collocational context vectors (Section 3.2) are associated to more than one sense of the target word and most strongly associated to the MFS.

increasing the p 1 threshold to 25 and 35 leads to a higher performance for CWW, since many of these words and collocations are filtered out.


So the closest pair of terms between these two sets is u 4 and x 11 .

whether the target relational clause h j (x, y) is entailed is determined by the relationship between x * and y * .


Different representations will impact how much a labeled adaptation algorithm can transfer information between domains.

in order to have a clear picture of what is the best labeled adaptation framework, it is necessary to analyze these two domain adaptation frameworks together.


First, unlabeled adaptation frameworks are necessary since many features only exist in one domain.

it is important to develop algorithms that find features which work across domains.


This shows that labeled adaptation and unlabeled are not independent.

it is important to combine these two aspects to see the real contribution of each adaptation framework.


Another possible explanation is that there is no clear relation between the context space and the target function: the context space is learned only indirectly by backpropagation.

due to the random initialization of the parameters and to data sparsity, many vectors of R might be blocked in some local maxima, meaning that similar vectors cannot be grouped in a consistent way and that the induced similarity is more "loose".


This relationship is however not expressed through the tying of the corresponding parameters; instead we let the prediction space guide the convergence of the context space.

we hope that it can achieve a convergence speed as fast as the one of the LBL model without degrading its prediction capacity.


For reasons to be discussed in Section 5, we instead prefer a soft-assignment strategy, inspired from the EM algorithm for the estimation of a mixture of Gaussians.

rather than the hard assignment A, we use a soft-assignment matrix P. P wk , interpreted as the probability of assigning word w to cluster k, is, essentially, proportional to exp{-d wk 2 /2σ 2 }, where d wk is the distance between the latent descriptor for w and the centroid, i.e., Gaussian mean, for k. Unlike the Gaussian-mixture model however, we use the same mixture coefficient and the same Gaussian width for all k. Further, we let the Gaussian width σdecrease gradually during the iterative process.


As is well-known, the EM algorithm for Gaussian mixtures reduces in the limit of small σ to the simpler K-means clustering algorithm.

the last few iterations of LDC effectively implement the hard-assignment K-meansstyle algorithm outlined in the previous paragraph.


Thus, the latent descriptors consist of the leftword and right-word contexts (recall that these are given by matrices L and R), mapped into left-label and right-label contexts through multiplication by the assignment matrix P, and scaled to unit length: With these latent descriptors in hand, we proceed with the M phase of the algorithm as usual.

the left mean µ L k for Gaussian k is the weighted average of the left latent descriptors L P (w), scaled to unit length.


Clearly, G is identical to F on any P that happens to be a hard assignment.

g is actually an extension of the objective function F to soft assignments.


Here we propose a new approach to this set of issues: start with a model that explicitly entertains ambiguity, and gradually constrain it so that it eventually converges to an unambiguous tagger.

although the algorithm uses probabilistic assignments, of Gaussian-mixture type, the goal is the construction of hard assignments.


With the addition of break indexes, we see marginal changes in most of the models; only the CRF tagger receives a significant boost.

models achieve performance gains from tagging shorter segments, but at the cost of limited usefulness of the prosodic breaks.


It can be shown that the model selection in our method is equivalent to a MAP inference, conducted under the assumption that the prior probability of a model decreases exponentially with its length (Goldwater, 2006).

the task that we are trying to accomplish is to conduct a focused search through the hypothesis space that will allow us to obtain an approximation of the MAP solution in a reasonable time.


Rather, when treated as two singlecharacter words, they can make sense in a sentence such as "请 (please) 自 (self) 来 (come) 取 (take)".

when using single-beam search to process "自来水 (tap water)", the two-character word candidate "自来" is likely to have been thrown off the agenda before the third character "水" is considered, leading to an unrecoverable segmentation error.


1 The mechanism of first-character decision of POS also boosts the efficiency, since the enumeration of POS is unecessary when a character is appended to the end of an existing word.

the complexity of each processing step is reduce by half compared to a method without POS prediction.


(Samuel et al., 1998;Stolcke et al., 2000;Chung, 2009)) has shown the effectiveness of structured classification models on sequential dependencies.

we expect similar effects with CRF and SVM-HMM.


As shown in Figure 1, zero anaphors may occur immediately to the left of规范/guide, 防止/avoid, 出现/appear, 根据 /according to, 结 合 /combine, 出 台 /promulgate, which cover the four true zero anaphors.

it is simple but reliable in applying above heuristic rules to generate zero anaphor candidates.


There are several usages of commas, and the positions to insert commas depend on these usages.

we grouped the usages of commas into nine categories, and investigated the appearance tendency for each category to find the effective features of machine learning by using Japanese newspaper articles.


Out of all the inserted commas, only 1.43% were inserted at positions which were not bunsetsu boundaries.

we analyzed only commas inserted at bunsetsu boundaries.


If there are too many commas at a short distance, the sentence becomes hard to read.

the number of characters between commas is expected to be not too small.


As we saw, even if the grammar is able to produce a full-span analysis for a given sentence, this analysis could still not be the correct one.

it is possible that a word could have a problematic lexical entry even if it only occurs in sentences which are assigned a full-span parse.


This phenomenon does not carry over to longer, more narrative-style documents that describe extended sequences of events, as found, for example, in biographies or descriptions of protracted geo-political events.

existing corpora are not ideal as development data for systems intended to work on such historical narrations.


We employ three human judges to manually assess the quality of 300 original sentences paired with each of the four paraphrases.

each judge assesses 1,200 paraphrase pairs in total.


We can see a positive correlation of adequacy and fluency with the overall score, and the correlation with adequacy is particularly strong.

higher adequacy and to a lesser degree higher fluency indicate higher paraphrase quality to the human judges.


In this approach, the unigram precision is always one (every word generated in the oracle hypothesis matches exactly one word in the reference).

to find the oracle hypothesis, we just have to maximize the recall, that is the number of words appearing both in the hypothesis and in the reference.


However, conventional evaluation metrics do not significantly penalize such word order mistakes.

locally optimizing these metrics leads to inadequate translations.


The middle word "node" between them corresponds to "because."


TER (Snover et al., 2006) was designed to allow phrase movements without large penalties.

these standard metrics are not optimal for evaluating translation between distant language pairs.


The 4th word "yesterday" in R1 becomes the 4th word in H1.


It has three increasing pairs: (3,4), (2,4), and (1,4).

since Kendall's τ is given by: the number of increasing pairs the number of all pairs − 1, In this case, we can obtain spearman's ρ as follows: "John" moved by d 1 = 2 words, "hit" moved by d 2 = 0 words, "Bob" moved by d 3 = 2 words, and "yesterday" moved by τ considers only the direction of the movement, whereas ρ considers the distance of the movement.


Sometimes, a word in the reference does not appear in the hypothesis, or a word in the hypothesis does not appear in the reference.

we cannot calculate τ and ρ following the above definitions in general.


On the other hand, i d 2 i = 5 2 × 6 + 2 2 + 7 2 × 4 = 350, and we obtain ρ = 1 − 350/ 12 C 3 = −0.591.

both Spearman's ρ and Kendall's τ give very bad scores to the misleading translation H0.


Perhaps, BP's poor performance was caused by the fact that most MT systems output almost the same number of words, and if the number exceeds the length of the reference, BP=1.0 holds.

we have to consider other modifiers for this overestimation problem.


In addition, BLEU is essentially made for precision.

precision seems the most promising modifier.


We developed our metric mainly for automatic evaluation of translation quality for distant language pairs such as Japanese-English, but we also want to know how well the metric works for similar language pairs.

we also use the WMT-07 data (Callison-Burch et al., 2007) that covers only European language pairs.


According to their table, the "sentence ranking" (or "rank") method obtained better agreement than "adequacy."


For example, in "IL-10 mediated expression" it is "IL-10" that mediates the expression.

we re-structure the dependency graph by changing the head of "IL-10" from "expression" to "mediated".


'NYC' and 'city', on the other hand, are mentions of the same geopolitical (GPE) entity of type named and nominal, respectively.

mD is a more general and complex task than the well known Named Entity Recognition (NER) task which aims solely at the identification and classification of the named mentions.


This segmentation pre-processing step consists of separating the normal white-space delimited words into prefixes, stems, and suffixes.

from a modeling viewpoint, the unit of analysis becomes the segments.


In doing so, we ensure that frequently performed operations can be expressed succinctly, so as not to complicate the rule set unnecessarily.

nERL rules for named entity recognition tasks are significantly more compact and easy to understand than the equivalent AQL rules.


For example, the definition of Organization in the CoNLL03 dataset contained a sports organization subtype, which was not considered when developing CoreNER.

we have used public data sources (e.g., Wikipedia) to collect and curate dictionaries of major sports associations and sport clubs from around the world.


designing an individual NERL rule remains a time-consuming "trial and error" process, in which multiple "promising" combinations are implemented and evaluated individually before deciding on a satisfactory final rule.


Also, there already exist systems that detect and classify these relations well.

even though these relations are directly relevant to our overall goal of social event extraction, we do not annotate, detect or classify these relations in this paper.


in contrast to SSTs, PT kernels compare many more substructures.


In contrast, the social event classification task does not suffer from data skewness because the INR and COG relations; both occur almost the same number of times.

sampling methods may not be applied for this task.


However, now we are interested in both outcomes of the classification, while earlier we were only interested in knowing how well we were finding relations (and not in how well we were finding "non-relations").

accuracy is the relevant metric (  Table 5: System for the task of relation classification.


These methods, however, assume that documents are represented via a flat feature vector (e.g., a bag-ofwords).

their ability to identify and exploit subjectivity (or other useful) information at the sentence-level is limited.


Each rationale is a text span that was identified to support (or explain) its parent document's sentiment.

these rationales can be interpreted as (something close to) a ground truth labeling of the explanatory segments.


If an incorrect translation is selected, it can distort the classification accuracy, by introducing erroneous features into the learning model.

our goal is to minimize the distortion during the model translation process, in order to maximize the classification accuracy in the target language.


However, because of the translation ambiguity of each word, a model in the source language could be potentially translated into many different models in the target language.

we think of the probability of the class of a target language document as the mixture of the probabilities by each translated model from the source language model, weighed by their translation probabilities.


For example, some Chinese people mistakenly say "see newspaper", partially because the translation of "see" co-occurs with "newspaper" in Chinese.

english verbs in the dictionary sharing more than two Chinese meanings are collected.


However search queries consist of keywords -relatively discrete and regardless of order.

except some proper nouns such as person's name, a lot of bigrams in the query log are formed in an adhoc way.


When it maps to the semantic word network, the title tends to contain the vertices scattered in the graph, while the context of the body part would add more semantically related vertices around the original vertices to strength the relations.

the body part has a higher density than the title part.


These topics are typically "focus" topics, which means users clearly pursue the unique event while they use these terms.

the density in top unigrams is very high.


Furthermore, when evaluating a particular rule with consequent C and antecedent A, it is natural to consider p(C|A) but, due to missing data, this absolute probability estimate is often misleading: in many cases C will hold given A but the fact C is not mentioned in the corpus.

to evaluate rules over extractions, we need to consider relative probability estimates.


Recently, (Baroni and Lenci, 2010) described a general framework of distributional semantic models that extracts significant contexts of given terms from large corpora.

a term can be represented by a vector of contexts in which it frequently appears.


As previously discussed in (Hovy et al., 2009), it is extremely difficult even for expert to manually construct and evaluate the correctness of the harvested taxonomies.

we decided to evaluate the performance of our taxonomization approach reconstructing WordNet Animals, Plants and Vehicles taxonomies.


", it will be better if we return the first paragraph from the corresponding Wiki article instead of a short phrase "current president of U.S.".


If this annotator cannot judge the type, this question will also be labeled as Other.

disagreements show up on eighteen percents of questions.


5700 bi-grams are represented by 10485 formulas.


This decision is controlled by the Bernoulli variable λ zn .

in step c.ii, we first generate a coin flip x n,2 from λ zn .


For word w n in document d, instead of sampling z n , x n,1 , x n,2 independently, we sample them as a block as follows: The above three equations can be normalized to form a 2 * K + 1 multinomial distribution: one component for generating a word from the ideology topic, K components for generating the word from the ideology-independent portion of topic k = 1, • • • , K, and finally K components for generating the word from the ideology-specific portion of topic k = 1, • • • , K. Each of these 2 * K + 1 cases corresponds to a unique assignment of the variables z n , x n,1 , x n,2 .

our Gibbs sampler just repeatedly draws sample from this 2 * K +1components multinomial distribution until convergence.


In this split each blog is represented in both training and test sets.

this dataset does not measure the model's ability to generalize to a totally different writing style.


We used 4 blogs (2 from each view) for training and held two blogs (one from each view) for testing.

this dataset measures the model's ability to generalize to a totally new blog.


The probability of B d,k does not depend on the value of v and thus can be absorbed into the normalization factor.

we only need to compute the following probability: All the integrals in (2) reduce to the ratio of two log partition functions.


They only cover the inquiry points, but do not provide information about the variants used in other locations.

a further preprocessing step interpolates the digitized point maps to obtain surface maps.


In these cases, it is likely that the most probable point is slightly outside the region, but that the largest part of the probability mass is still inside the correct region.

we tested another approach: we defined the predicted dialect region as the one in which the average probability is higher than   the average probability in any other region: This metric effectively boosts the performance on the smaller regions, but comes at a cost for larger regions (Table 7).


We assume the selected synset represents the meaning of the original word, and those synonyms in the synset which are not annotated as positives must have a certain degree of mismatch to the context.

from this example, 'balance', 'residue', 'residuum' and 'rest' are extracted as negatives to test whether our synonym checking method can pick out bad substitutions from a set of words sharing similar or the same meaning.


we can only encode at most 2 bits using a typical synset).

we restrict the chromatic number of a synonym graph G to 1 < χ(G) ≤ 4, which implies the maximum size of a synset is 4.


When χ(G) = 3, the overlapping set's size is either 2 or 3, which cannot exhaust the 2-bit coding space although codewords '00', '01' and '10' are initially assigned to each vertex.

only the most significant bits are used to represent the synonyms, which we call codeword reduction.


However, supervised methods require the manual annotation of training data with parse trees, a process which is expensive and time consuming.

for domains and languages with minimal resources, unsupervised parsing is of great importance.


We are interested in estimating the posterior p(θ, z | x).

variational inference transforms this problem into an optimization problem where we try to find a distribution q(θ, z) from a restricted set Q that minimizes the KL-divergence between q(θ, z) and p(θ, z | x): Rearranging the above yields: where F is defined as F is a lower bound on likelihood.


The only factor affected by the expectation constraints is q(z).

recall from the previous section that the update for q(z) is performed via gradient search on the dual of a constrained minimization problem of the form: we first compute the update for q (z): where N is the total number of sentences, len(n) is the length of sentence n, and index h(nj) refers to the head of the jth node of sentence n. Given this q (z) a gradient search is performed using (6) to find the optimal λ and thus the primal solution for updating q(z).


Unlike the universals from Table 1, these rules alone are enough to construct a full dependency tree.

they allow us to judge whether the model is able to improve upon a human-engineered deterministic parser.


knowledge of the true languagespecific rule proportions is not necessary for high accuracy.


The first baseline is based on the hypothesis that the existence of polarized words is a strong indicator that the sentence has an attitude.

we use the number of polarized word in the sentence, the percentage of polarized words to all other words, and whether the sentences has polarized words with mixed or same sign as features to train an SVM classifier to detect attitude.


I might search for either Kristina Toutanova or Toutanova Kristina).

spelling correction techniques that rely crucially on bigram and higher order language models will fail on queries with a different word order than what is observed in the query log.


Clearly, our success in finding the right name suggestion for the query in the NAME MATCHING stage depends crucially on our success in getting the right name suggestion in the list of candidates produced by the NAME BUCKETING stage search.

we need a name similarity search technique that can ensure very high recall without producing too many candidates.


g) as follows: where As before, we center Φ and Ψ to getΦ andΨ respectively.

we get the following optimization problem: s.t.


Each name was represented as a feature vector over character bigrams.

the name token Klein has the bigrams {•k, kl, le, ei, in, n•} as the features.


It is natural to identify functions based on triples extracted from text instead of analyzing sentences directly.

as our input, we utilize tuples extracted by TEXTRUNNER (Banko and Etzioni, 2008) when run over a corpus of 500 million webpages.


Freebase currently has over 12 million entities in over 1,000 typed lists.

this intersection retains significant portions of the useful data, and gets rid of most of anaphora and synonymy issues.


Each element in the chain defines a distribution over words, and acts as the mean of the distribution over the subsequent element in the chain.

each element in the chain can be thought of as introducing some additional corruption.


Throughout we will write x to indicate the expectation of x under the variational distribution Q.

the vector mean of the distribution q(η jk ) is written η jk , while the variance (uniform across i) of q(η) is written V(η jk ).


Supervised Latent Dirichlet Allocation In a more subtle version of the mixture-of-unigrams model, we model each author as an admixture of regions.

the latent variable attached to each author is no longer an index, but rather a vector on the simplex.


There has been a long history in combinatorial optimization of methods that exploit structure in complex problems, using methods such as dual decomposition or Lagrangian relaxation (Lemaréchal, 2001).

far, however, these methods are not widely used in NLP.


Without the z(i, j) = y(i, j) constraints, the objective would decompose into the separate maximizations z * = argmax z∈Z f (z), and y * = argmax y∈Y h(y), which can be easily solved using dynamic programming and MST, respectively.

it is these constraints that complicate the optimization.


Given the i'th example in the training set, (x (i) , y (i) ), the perceptron updates are as follows: The first step involves inference over the set Z, rather than Y as would be standard in the perceptron.

decoding during training can be achieved by dynamic programming over head automata alone, which is very efficient.


Note, however, that the LP relaxation optimized by dual decomposition is significantly tighter than Z.

an alternative approach would be to use the dual decomposition algorithm for inference during training.


Initial experiments showed it is hard for learners to discover which posts have multiple links, largely due to the sparsity of multi-headed posts (which account for less than 5% of the total posts).

only the the most recent link for 3 http://maltparser.org/userguide.html# parsingalg 4 Classification accuracy = F-score at the thread-level, as each thread is assigned a single label of correct or incorrect.


Assuming as in Och (2003) that error count is additively decomposable by sentence-i.e., E(r S 1 , e S 1 ) = s E(r s , e s )-this results in the following optimization problem: 2 The quality of this approximation is dependent on how accurately the N -best lists represent the search space of the system.

the hypothesis list is iteratively grown: decoding with an initial parameter vector seeds the N -best lists; next, parameter estimation and N -best list gathering alternate until the search space is deemed representative.


4 Algorithms presented in this paper assume that D is unrestricted, therefore we cannot afford to build any convex hull explicitly.

we turn to linear programming (LP), for which we know algorithms (Karmarkar, 1984) that are polynomial in the number of dimensions and linear in the number of points, i.e., O(N T ), where T = D 3.5 .


Although the search space is different than that of Och's algorithm, it still relies on one-dimensional line searches to reflect, expand, or contract the simplex.

it suffers the same problems of one-dimensional MERT: feature sets with complex non-linear interactions are difficult to optimize.


To compute precision at rank k, we average the judgments.

a system can receive a score of 0.0 (both judgments negative), 0.5 (judges disagree), or 1.0 (both judgments positive) for each returned answer.


(Paşca, 2004;Snow et al., 2006;Kozareva and Hovy, 2010) have shown that complete lists of semantic classes and instances are valuable for the enrichment of existing resources like WordNet and for taxonomy induction.

researchers have focused on the development of methods that can automatically augment the initially extracted class-instance pairs.


Let C p denote the structural constraints that are "local" to the phenomenon.

for verb SRL, these would be the constraints defined in the previous section, and for preposition role, the only local constraint would be the constraint (4) defined above.


However, pattern-based methods usually suffer from inconsistency of parent-child relationships among the concepts, while clustering-based methods often result in low accuracy.

by directly utilizing these methods to generate an aspect hierarchy from consumer reviews, the resulting hierarchy is usually inaccurate, leading to unsatisfactory review organization.


It has been shown that simply extracting the frequent noun terms from the Pros and Cons reviews can get high accurate aspect terms (Liu el al., 2005).

we extract the frequent noun terms from Pros and Cons reviews as features, then train a one-class SVM (Manevitz et al., 2002) to identify aspects from the candidates.


The learning problem is formulated as follows, where η and γ are tradeoff parameters.

the optimal solution of w can be obtained as we can compute the semantic distance between each two aspects according to Eq.(1).


As shown in Figure 5, the performance increases when larger proportion of the initial hierarchy is used.

we can speculate that domain knowledge is valuable in aspect hierarchy generation.


(2008) can identify implicit aspects without hand-crafted rules based on mutual clustering.

we adopt Su's method as the baseline here.


However, syntactic parsers were usually costly and impractical when dealing with large scale of text.

in additional to lexical features, we incorporated sequential and semantic information in proposed method for discourse relation classification.


Cue-phrase-based patterns could find only limited number of discourse instances with high precision (Marcu and Echihabi, 2002).

we could not rely on cue-phrase-based patterns alone.